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Prompt Once, Reuse Forever: Claude Skills for WebDataRocks Reporting

We explained earlier how to build a working WebDataRocks pivot table from a single Claude prompt. It’s a quick way to test the component and see how it works without setting up a full project. However, that’s great for a one-off report, but if you’re pulling the same kind of summary every week, or asking Claude to compare periods over and over, retyping the full setup each time gets old fast.

But what if WebDataRocks could be part of your regular reporting workflow with Claude Skills? Instead of rebuilding the same report from scratch, you can save the workflow once and reuse it whenever you need it.

So, here we’ll look at how to “package” those requests into Claude Skills, so you describe the workflow once and reuse it on demand. This time, we’re working with real economic data, since that’s closer to what an analytical report usually looks like.

From one-time prompts to something you don’t have to retype

When you write prompts, every step starts from scratch: describe the fields, describe the layout, describe the formatting rules. That’s fine when you’re exploring an idea. But it’s more difficult when this idea is repetitive, for example, every Monday morning.

A Claude Skill is a saved set of instructions, a short markdown file with a name, a description, and the steps Claude should follow, that Claude picks up automatically once your request matches what the skill is for. You’re not pasting the same links and dataset shape into chat every time; you save them once, and from then on, a short request like “run this week’s digest” is enough.

More info about Claude’s skills

If you’ve never used this feature of Claude, Skills are located under Customize > Skills in your account. At a minimum, a skill is:

• A name and a description: the description is what Claude matches against your request, so it needs to say plainly what the skill does and when it applies
• Markdown instructions: the actual steps, written the same way you’d explain them to a new teammate

So now we know all the needed basic info about Claude Skills, so we’ll walk through three skills that are worth setting up if you work with WebDataRocks reports regularly. Also, it can be a great start to implementing pivot tables into your work.

Skill 1: The daily/weekly/monthly data digest

The idea: instead of asking “what happened this week” from scratch, you ask for the digest, and Claude rebuilds the same report, pulls out what actually moved, and summarizes it — no new dataset, no re-explaining the layout.

In this example, we’ll use the Frankfurter API to get live exchange rates.

---
name: "webdatarocks-data-digest"
description: "Fetch the latest exchange rates from the Frankfurter API,
update the WebDataRocks report and summarize what changed. Use when the
user asks for a "digest," "recap," or "what changed" for their exchange
rate report."
---

# WebDataRocks Data Digest

1. Fetch the latest exchange rates

Fetch the current exchange rates from the Frankfurter API:

https://api.frankfurter.app/latest?from=EUR

Use the currencies already defined in the report.

2. Build or update the WebDataRocks pivot

If there is no existing WebDataRocks report, build an interactive WebDataRocks pivot table in the Claude artifact.

Load WebDataRocks from jsDelivr:

* CSS: https://cdn.jsdelivr.net/npm/webdatarocks/webdatarocks.min.css
* Core script: https://cdn.jsdelivr.net/npm/webdatarocks/webdatarocks.js

Create the report using the fetched exchange-rate data.

Keep the report structure simple and consistent so it can be compared across runs:

* Rows: Currency
* Measure: Exchange Rate
* Aggregation: Sum
* Toolbar: enabled
* Container height: 520px

If an existing report is available, update its data with the new exchange rates instead of rebuilding the report. Keep the same rows, columns, and measure structure.

3. Compare with the previous fetch

Compare the new exchange rates with the last fetched values.

Recall the previous values from the conversation when available. If there are no previous values, state that this is the first run and skip the comparison.

4. Summarize what changed

Write 3–5 plain-language bullets describing:

* which currencies moved the most;
* whether each moved up or down;
* any other notable changes.

Do not simply repeat the raw exchange-rate table.

5. Attach the updated report

Provide the updated interactive WebDataRocks pivot table as a Claude artifact alongside the summary.

Trigger it with something as short as:

Fetch the latest USD, GBP, JPY, CHF, CNY, AUD, CAD, INR, BRL, and MXN rates against EUR, group them by region, and give me the digest

Skill 2: Comparing two periods on request

This one’s for the “how does X stack up against Y” question. It can be anything: two months, two store formats, two anything that exists in your table. Rather than rebuilding the report and manually analyzing two columns/rows/values, the skill does it for you.

---
name: "webdatarocks-period-compare"
description: "Compare two periods, categories, or filter values in a WebDataRocks report and show the absolute difference and percent change. Use when the user asks to “compare,” “vs.,” or wants to see how one period or segment stacks up against another."
---

1. Identify the two periods, categories, or filter values from the user's request. If they are not specified, ask which two values to compare.

2. Build or update an interactive WebDataRocks pivot table in the Claude artifact.

   If there is no existing WebDataRocks pivot table, create one from the available data. Do not use Claude's native charts or tables instead.

   Load WebDataRocks from jsDelivr:

   - CSS: "https://cdn.jsdelivr.net/npm/webdatarocks/webdatarocks.min.css"

   - Core script: "https://cdn.jsdelivr.net/npm/webdatarocks/webdatarocks.js"

   Configure the pivot with:

   - the category being compared in rows;

   - the two periods or values in columns;

   - the relevant numeric field as the measure;

   - the appropriate aggregation;

   - the toolbar enabled;

   - a container height of 520px.

3. Keep the original data and report structure whenever an existing report is available. Do not regenerate the underlying data unnecessarily.

4. Add calculated values for:

   - Absolute Change = New Period − Previous Period

   - Percent Change = (New Period − Previous Period) / Previous Period × 100

5. Format the change values:

   - increases: green text with a "+" prefix;

   - decreases: red text with a "-" prefix.

6. If multiple categories are compared, identify the biggest increase and biggest decrease.

7. Summarize the main changes in 3–5 plain-language bullets. Do not simply repeat the raw pivot table.

8. The final artifact must contain an actual interactive WebDataRocks pivot table. Do not replace it with a custom HTML table, chart, or another visualization library.

Trigger prompt:

Compare exchange rates this year vs. last year

You get a report with both months side by side, a column showing the difference, and a quick note on which store format changed the most, instead of comparing two separate tables and doing the math yourself.

Skill 3: Formatting recommendations for an existing pivot

This one assumes you already have a pivot table built and just want a second opinion on how to present it, for example, which values actually better to be highlighted, and for which use a conditional formatting rule.

---
name: "webdatarocks-format-advisor"
description: "Review an existing WebDataRocks pivot table and recommend or
apply conditional formatting to highlight values that stand out. Use when
the user asks Claude to “highlight,” “make the outliers stand out,” or
wants formatting suggestions for a report."
---

1. Inspect the existing pivot

Find the existing WebDataRocks instance and inspect the measure currently displayed in the pivot.

Use the actual values currently available in the pivot/report. Do not invent sample data or thresholds.

2. Determine the formatting threshold

Calculate the mean and standard deviation of the relevant measure values.

Use values more than one standard deviation above or below the mean as candidates for highlighting.

The rule must be based on the actual data distribution:

* Lower bound = mean − standard deviation
* Upper bound = mean + standard deviation

Do not use arbitrary values such as $2,000 or 100 unless they are explicitly requested by the user.

3. Apply conditional formatting directly

If the user asks to highlight, make values stand out, show outliers, or otherwise requests formatting to be applied, apply the formatting immediately.

Do not ask for confirmation.

Use WebDataRocks conditional formatting on the existing pivot table. Apply the rule to the relevant measure only.

The formatting should make matching cells visually stand out using:

* bold text
* a visible background/text color

The conditional formatting must be implemented through the existing WebDataRocks report configuration or WebDataRocks API used by the artifact.

Do not replace the pivot with another library or create a second pivot.

4. Modify the artifact, not just the explanation

After determining the rule, update the code/configuration of the current Claude artifact so that the change is visible in the rendered WebDataRocks pivot.

The final artifact must contain the conditional formatting rule.

Do not merely:

* explain what code the user could add
* return a code snippet without applying it
* describe the formatting without changing the pivot
* create a new standalone HTML example
* create a second WebDataRocks instance

The user’s existing pivot is the target of the change.

5. Preserve the existing report

Keep the existing:

* WebDataRocks instance
* dataset
* rows
* columns
* measures
* filters
* layout
* theme
* other report settings

Only add or modify the conditional formatting required for the user’s request.

6. Verify the result

After updating the artifact, verify that:

1. the WebDataRocks pivot still renders;
2. the conditional formatting is attached to the correct measure;
3. cells matching the calculated rule are visually highlighted;
4. no unrelated report configuration was removed.

If the artifact uses an existing report object, modify that configuration instead of creating a new report from scratch.

7. Response

Keep the response short.

State that the formatting was applied and briefly mention the threshold used, for example:

“Applied conditional formatting to values more than one standard deviation from the mean. The rule is based on the current data distribution.”

Trigger prompt:

Look at this pivot and highlight anything that stands out

Claude looks at your numbers before suggesting a rule. It also explains why it chose that number, so you can change it before using the rule.

Adapt these to your workflow

By the way, you are not limited to these 3 skills demonstrated in this article. You can combine them into one skill. For example, one that fetches data, compares periods, and highlights outliers all at once. You can also edit the Markdown file directly. And if none of these three skills fit your use case, the same approach still works: write down the steps you’d like to implement, save them, and you have a skill for any task you do repeatedly.

Where Claude Skills help

• Spend less time typing the same setup for recurring reports
• Keep formatting rules the same across reports and for teammates using the same skill
• Explore reporting workflows before adding anything to a real app

They’re still not a substitute for a production integration, because a skill runs within a chat conversation with whatever data you give it. Connecting a live data source, syncing it outside of Claude, or presenting it to real users still means the actual WebDataRocks setup in your project.

Try It Yourself

Pick whichever of the three matches something you already ask for on repeat, save it as a skill, and try triggering it with a one-line request instead of a full prompt. If none of them quite fit, the same shape works for other repeat asks — describe the steps once, and let the skill remember them for you.

AI develops and grows really fast nowadays, and the good news is that you can use it to your advantage when working with WebDataRocks. More precisely, you can build a working pivot table in ChatGPT before you’ve opened a single project file. The catch isn’t the pivot table library — WebDataRocks is light enough that describing a report in plain English gets you a working grid in one message. The catch is a handful of small, predictable things that go wrong if you don’t know they’re coming.

This article covers everything you need to know to get started with WebDataRocks in ChatGPT, using an online bookstore’s sales data as a practical example.

Before we start, we have a step-by-step tutorial on using Claude with WebDataRocks. If you’re interested in this topic, we highly recommend checking it out here.

Use jsDelivr for a smoother Canvas experience

If you’ve already been scrolling through our docs, you’ve probably noticed that WebDataRocks’ documentation points to cdn.webdatarocks.com. When you use that URL in a ChatGPT Canvas prompt, the preview may stay blank because Canvas previews run in a sandbox, and their access to external web resources can be restricted.

In our testing, loading WebDataRocks via jsDelivr is more reliable in the Canvas preview. So, instead of using the URL from the documentation, tell ChatGPT to load the same files from jsDelivr:

Load the CSS from "https://cdn.jsdelivr.net/npm/webdatarocks/webdatarocks.min.css" and the script from "https://cdn.jsdelivr.net/npm/webdatarocks/webdatarocks.js". 

Getting a working table in one message

Fields, layout, and measure in one prompt, rather than the library first and the report shape as a follow-up:

Build an interactive WebDataRocks pivot table. Load the CSS from "https://cdn.jsdelivr.net/npm/webdatarocks/webdatarocks.min.css" and the script from "https://cdn.jsdelivr.net/npm/webdatarocks/webdatarocks.js".

Generate 100 records of online bookstore sales as a JSON array with fields: Genre, Format (Print, Ebook, Audiobook), City, Units Sold, Revenue in USD, Month.

Configure the report: rows = Genre, Format; columns = City; measure = sum of Revenue. Enable the toolbar. Set the container height to 500px. 

Vague phrasing gets you a vague report

Important to mention that there’s a real gap between prompts that sound similar but land differently. “Show revenue by genre and city” is the kind of line people type first — and it usually gets you a table, just not one with a defined row/column split, so half the time you end up with everything crammed into rows and no columns at all.

Compare:

Weak: "Show me revenue by genre and city."
Works: "Rows = Genre. Columns = City. Measure = sum of Revenue."

The difference is that the second version maps directly onto WebDataRocks’ report object — rows, columns, and measures are named fields in that config, not just descriptions of intent. Say it the way the library thinks about it, and ChatGPT doesn’t have to guess which axis you meant.

Changing the slice without losing the data

Ask for a “new pivot table” to see a different angle, and ChatGPT will regenerate the underlying records and layout — so your before-and-after screenshots will no longer be comparable. Ask for an edit instead:

Update the pivot table you just generated. Keep everything exactly the same. Only change the report layout to:

Rows: Format, then Genre
Columns: Month
Move City to the report filter
Measure stays Revenue, sum aggregation 

Give me the complete updated HTML.

Same 100 rows, new angle. “Update the existing report” is the phrase doing the work — use it every time.

Calculated fields without needing the syntax

Add a calculated value "Revenue per Unit" = Revenue / Units Sold, shown as currency with 2 decimals, as a second measure next to Revenue.

Format Revenue cells with a thousands separator and a "$" prefix. Highlight cells where Revenue is above $1,500 in bold green, and below $200 in red. 

Describe the outcome, not the function name — ChatGPT maps it to WebDataRocks’ calculated value and conditional formatting options on its own.

Try it yourself

Now it’s time to experiment. Take the first prompt from this article, swap the fields for a dataset you actually work with, and see what ChatGPT can build. Then experiment with the report itself: you can do much more than described in this article. For example, try switching themes, applying conditional formatting, adding filters, sorting values, adding/deleting the toolbar, etc.

You can describe each change in plain English and see how ChatGPT translates it into a WebDataRocks report. If something doesn’t work as expected, try to refine your prompt.

WebDataRocks is free to experiment with, so there’s no real cost to trying different report ideas before you write any setup code.

Lately, LLMs have advanced to the point where they can not only answer questions or write code but also build working interfaces. This makes them a handy place to quickly test ideas for reports and data analysis. Instead of starting with a project and writing the code yourself, you can start with a simple prompt in your AI assistant. For example, you can ask it to build an interactive WebDataRocks pivot table right in the chat.

WebDataRocks is a free JavaScript pivot table built for exactly this kind of quick reporting job. It’s simple enough for an AI assistant to set up from one prompt and free, so you can seamlessly experiment and try out your ideas. Describe the report you want, and Claude hands back a working, interactive grid in the same message.

Here we’ll show how to build one using made-up coffee shop sales data. Step by step, with copy-paste prompts that you can use as a base for your own projects.

Why this is worth doing before you write any real code

Analysts and developers use pivot tables for the same reason: to see how a metric breaks down across two or three dimensions without writing a query for every combination. Normally, that means opening a project, wiring up a data source, and configuring the grid by hand.

Skipping straight to a chat window compresses that into a conversation. You’re not shipping anything, you’re checking whether a particular row/column split tells you what you think it will, or showing a colleague a rough mockup of “what if we grouped by store type instead.” Once the shape is right, translating it into a real WebDataRocks integration is copy-paste work, because the report configuration object is identical either way.

One setup detail to keep in mind

WebDataRocks’ documentation points you to cdn.webdatarocks.com by default. That domain won’t load inside most of AI assistants’ preview pane — each of these environments only executes scripts pulled from a short allowlist of public CDNs, and WebDataRocks’ own domain isn’t on it. So, in this tutorial we’ll use WebDataRocks files from jsDelivr instead.

Step 1: Describe the data and the first slice in one go

Rather than asking for the library first and the data second, put everything in one message — the field list, the row/column split, and the measure. That’s the fastest way to a usable result on the first try.

Build an interactive WebDataRocks pivot table in the Claude artifact.
Load the library from jsDelivr: CSS from
"https://cdn.jsdelivr.net/npm/webdatarocks/webdatarocks.min.css",
and the core script from
"https://cdn.jsdelivr.net/npm/webdatarocks/webdatarocks.js".
Generate 80 records of coffee shop sales as a JSON array with fields:
City, Store Format, Product Category, Units Sold, Revenue in USD, Month.
Configure the report: 
rows = City, 
Store Format; 
columns = Product Category;
measure = sum of Revenue. 
Enable the toolbar. 
Set the container height to 520px.

What comes back is a built-in pivot table already sliced: city and store format down the rows, product category across the top, revenue totals filling the cells.

One prompt, one working report. The only real work was clearly describing the fields and the slice. Also, if it’s more convenient, you can split it into different prompts and watch your grid creation gradually.

Step 2: Change the slice without losing the data

Here’s a mistake worth avoiding: asking for a fresh pivot table every time you want a different view. That regenerates the underlying dataset, too, so your numbers change between screenshots, and nothing stays comparable. Moreover, it wastes many more of your generating tokens.

Instead, refer back to what’s already there:

Update the existing report, don't regenerate the data:
- Rows: Product Category, then City
- Columns: Month
- Move Store Format to the report filter
- Keep Revenue as the measure, sum aggregation

WebDataRocks keeps the entire layout: rows, columns, filters, measures, in a single report object, so this kind of request is edited rather than rebuilt. Same 80 records, but new angle on them.

Step 3: Layer on a calculated metric and some formatting rules

Once the slice looks exactly as you expect, it’s possible to try a calculated value too, something that isn’t in the raw data at all.

Add a calculated value "Avg Revenue per Unit" = Revenue / Units Sold,
formatted as currency with 2 decimal places.
Add it as a second measure next to Revenue.
Format all Revenue cells with a thousands separator and a "$" prefix.
Where Revenue is above $2,000 in bold green text, and cells below $300 in red.

Mentioning a function name or a syntax detail isn’t necessary at all. You can describe the outcome you want, and Claude picks the matching WebDataRocks option or callback on its own.

Step 4: Try a different look or a different source

What about more styling? You can apply any theme you want with only one prompt.

Switch WebDataRocks theme to green, loading the green theme CSS from the same jsDelivr package.

However, we recommend checking the available built-in themes provided by WebDataRocks to avoid unexpected errors and data regeneration.

It also runs outside Claude

Everything above was built in Claude, but none of the prompts are Claude-specific. It can also be done in ChatGPT or Grok. So, try to experiment with data in your favorite AI assistant!

Where the demo ends, and real integration begins

Think of this as a quick way to test ideas, not a way to build a production app. It works well for:

•  Testing different row and column layouts before adding them to your real project
•  Showing a stakeholder a quick report with sample data while you’re discussing an idea
•  Learning how the WebDataRocks report API works without going through the docs step by step
•  Creating a working demo in just a few minutes

But if you want to connect real data, match your app’s design, or use the pivot table for real users, you’ll need to move to a proper project and install WebDataRocks there. That’s a separate step from the quick demo we built here.

Your turn

Take the Step 1 prompt, swap the coffee-shop fields for any dataset you want, and see what comes back. WebDataRocks is free to use for exactly this kind of exploration, so there’s no reason not to try it before you write a single line of setup code.

The human brain processes visual cues faster than written ones. According to Visme (2022), 90% of the information communicated to our brain is visual. This is why utilizing visual aids in problem-solving helps people to find solutions faster. Visual problem-solving refers to the process of finding solutions to problems with the use of visual aids like flowcharts, diagrams, maps, infographics, etc. In simple terms, you solve problems using visual aids. This method helps people understand the situation better as visual tools showcase how elements relate to each other more clearly. 

The significance of visual data representation lies in its ability to make people see and understand data easier and better. With visual elements, complex data becomes easier to grasp so that everyone can understand them no matter their level of expertise. Visual data representation also helps businesses to gather insight from vast amounts of data. It simplifies data representation and allows it to be used efficiently by entrepreneurs.

The Role of Visual Data Representation in Problem-Solving

1. Communicating Complex Information

Visual data representation can transform complex ideas into simple information, making them easier to communicate to other people. It can bridge gaps in the communication process, especially when working with people from different backgrounds and levels of expertise. Visual data representation makes it faster to disseminate concepts, findings, and solutions to others, enabling efficient collaboration within teams.

2. Enhancing Understanding and Analysis

Visual data representation makes individuals understand and analyze complicated problems more easily. Visual tools to convey data and abstract ideas can make complex concepts easier to grasp, particularly when handling and analyzing essential aspects and relationships within a problem. It aids in breaking down difficult problems transforming them into smaller components that are easier to manage and understand.

3. Enabling Informed Decision-Making

Visual data representation enables informed decision-making. Visualization tools help people evaluate and compare different concepts or solutions, allowing for more informed decision-making. Its decomposition of complex problems into manageable components allows for a more structured problem-solving process. Furthermore, it reduces the probability of having cognitive biases that may affect choices.

Types of Visual Data Representation

1. Graphs and Charts

a. Line Graphs

A line graph is a chart that displays a line joining several points or a line that shows the relationship between two points. It is commonly used to display data or information that changes over time. It consists of the horizontal “x-axis” and vertex “y-axis”.

b. Bar Charts

A bar chart is a graph that displays categorical data using horizontal or vertical bars with heights or lengths in proportion to the values they represent. It is commonly used to present categorical and discrete or continuous variables grouped in class intervals. 

c. Pie Charts

A pie chart is a circular graph that is divided into slices or sectors to present numerical proportions. The length or size of each slice or section is proportional to the data it is meant to represent.

2. Infographics

a. Visual Storytelling Through Data

An infographic is a visual image used to represent information, data, or knowledge. It tells a story visually through data, enabling a more compelling way to make people learn and understand information. An infographic with good visual storytelling includes great content, typography, colors, symbols, grids, hierarchies, icons, white space, and more. You can create and enhance your infographics by using a background remover to efficiently remove unnecessary backgrounds from the visual elements in your infographic so people can focus more on the visual storytelling and not get distracted. 

b. Combining Text and Graphics for Clarity

Infographics provide clarity by combining text and graphics. It helps in summarizing complex situations, managing information, heightening engagement, and increasing comprehension. By having graphics and text together, infographics give a clearer view of the data or information presented so different individuals can easily understand them.

3. Heatmaps and Treemaps

a. Identifying Patterns in Data Density

A heatmap is a 2D data visualization tool that represents data using colors to show density or magnitude. Heatmaps are commonly used in marketing, website analytics, and consumer behavior. For example, heatmaps are used to display the behavior of users on a website through pattern recognition. Particular colors are used to show the website’s frequent and infrequent clicks.

b. Hierarchical Representation of Information

A treemap is another data visualization tool used to showcase the hierarchical representation of information. It includes nested rectangles which represent categories within the area in a “tree”. Individuals can compare and display the hierarchy, quantity, and pattern based on their space on the chart.   

5 Tips on How to Visualize Data to Facilitate Problem-Solving

1. Identify Trends and Patterns

a. Recognizing Correlations in Data

You can visualize data by recognizing their correlations. Once you know their relationship, you get to group them, identify patterns and trends, or display their correlation visually to enable faster communication and more efficient problem-solving.

b. Uncovering Anomalies and Outliers

Anomaly and outlier detection is another power of data visualization. You get to uncover anomalies and outliers that pertain to data that are significantly different from other data. By detecting these, you get to know what area you should focus on during problem-solving.

2. Simplify Complex Information

a. Condensing Large Datasets

Data visualization tools like charts allow you to break down or condense large datasets into smaller and easy-to-understand formats. You categorize data or divide them into groups instead of just seeing them as a whole. By simplifying large amounts of data, it is easier to get insights and show findings you won’t get just by looking at the raw datasets.

b. Enhancing Data Accessibility for Stakeholders

By making complex data easier to understand, you enhance their accessibility for stakeholders. Your stakeholders, whatever their profession or level of expertise, can easily grasp the information you want to deliver because you have made the data accessible enough for them to understand quickly. This allows for more effective communication between you and your stakeholders.

3. Support Hypothesis Testing

a. Visual Confirmation or Refutation of Hypotheses

Data visualization tools enhance hypotheses by confirming, refuting, challenging, or expanding the ideas based on the existing data. Visualization can showcase if the hypotheses are affected by the division of shapes, data outliers, and particular subgroups.

b. Iterative Problem-Solving Processes

You can discover contradictory or unexpected findings to your hypotheses during your iterative problem-solving process through data visualization tools. You test your hypothesis repeatedly until you achieve your desired result. With visualization, you can continually improve your hypothesis.

4. Tools and Software for Visual Problem-Solving

a. ClickUp 

ClickUp is a visual problem-solving software that ensures the efficiency of your team’s collaboration. It makes sure that the members of your team are on the same page all the time. A live collaboration tool lets you create or edit documents with your team. It also includes virtual whiteboards to aid you in your problem-solving processes. The whiteboard feature helps your team brainstorm or generate ideas to solve problems you want to address. Mind maps are also available on the site, which lets you identify significant relations, gather useful insights, and execute creative approaches through mapping the connections between datasets and concepts. 

b. Hive 

Hive is a data visualization tool that provides insightful solutions to your problem-solving processes. With Hive, you can get your team to face difficult challenges and improve their problem-solving abilities. It’s an engaging visual problem-solving platform that makes you see what your team members are doing. Its collaborative work management feature allows you to work with your team to address and solve issues efficiently. It has a user-friendly interface, project hierarchy, Gantt view, and Kanban view, which help you better manage your timelines and progress.

c. Tableau 

Tableau is one of the most popular tools for visual problem-solving big companies use. With the platform, you can make several types of maps, graphs, and charts and connect to various sources of data. It efficiently produces visualizations from small-scale to large-scale datasets commonly used for artificial intelligence and machine learning. Tableau is known for its high-quality performance, user-friendly interface, and great data visualization tools.

d. Trello 

Trello is a visual problem-solving platform that effortlessly streamlines your work processes by organizing tasks using the drag-and-drop feature. Its collaboration and communication tools let you prioritize tasks and responsibilities efficiently. It has a map that exhibits an overview of your projects so you can see tasks, progress, and dependencies visually. Trello ensures you can solve issues through its effective task-management tools

e. Asana

Last on the list is Asana. It is another well-known visual problem-solving platform that improves your decision-making efficiency. It can be used for several small projects and for teams without complicated workflows. It has a customizable workflow feature aids your team in their unique needs by automating repetitive tasks. It has Kanban boards, Gantt charts, and other data visualization tools used to enhance your problem-solving processes. Lastly, it can be integrated with popular tools like Google Drive, Microsoft, Zoom, etc.

5. Integration with Business Intelligence Systems

a. Leveraging Data for Strategic Decision-Making

Data visualization tools help you leverage data to have strategic decision-making. Visualized data integration allows you to access the data you need making it easier for you to make informed decisions for your business, fueling your business’ competitive intelligence.

b.  Real-Time Analytics and Reporting

Real-time analytics and reporting involve gathering live data and sending it to users as it happens. You relay information based on the latest happenings so you or your managers can decide quickly based on the visualized real-time data you reported.

Practical Applications of Visual Problem-Solving

1. Business and Financial Analysis

a. Visualizing Key Performance Indicators (KPIs)

You can use visual problem-solving in a practical setting by using it for your business and financial analysis. You can apply it by visualizing your Key Performance Indicators or KPIs. To do it, you can simply compare a base value to a target value. You can also use charts and graphs to efficiently present the quantitative nature of your KPIs so you can bring out insightful observations from your team. It simplifies evaluating your project or campaign by clearly seeing the KPIs in place.

b. Forecasting and Trend Analysis

Another way to apply visual problem-solving is to forecast and analyze trends. Using visual tools like line and bar graphs, you see trends over time and draw forecasts from them. You see and build a pattern recognition over time, allowing you to create assumptions that you can use in your next problem-solving sessions. With visual tools, you get the idea of what may happen in the future based on your previous reliable visual data.

2. Scientific Research and Exploration

a. Visualizing Experimental Data

Data visualization plays a significant role in scientific research and exploration. It is applied by visualizing experiments to make them easier for stakeholders to understand. Using graphs, charts and other tools allows stakeholders to efficiently determine issues, identify new trends, and gain insightful information from hard-to-grasp experimental or novel data.

b. Communicating Research Findings Effectively

With visual aids, you also get to communicate your research findings effectively. You can use line graphs to see the changes in trends over time. You can use pie charts to show the percentage of an item from a whole and draw conclusions from them. Visual aids make it easier for you to convey the results of your research to other people.

Best Practices for Visual Problem-Solving

1. Choosing Appropriate Visualization Techniques

To maximize the potential of visual problem-solving, you need to pick the appropriate visualization techniques for your team. You have to ensure that the strategy you employ will help you and make your job easier rather than burden you. By choosing the appropriate data visualization techniques, you can efficiently solve even complex problems with your team. You ensure that your team members are knowledgeable of the strategy so you can reap its full potential.

For teams working with structured datasets, WebDataRocks is a free JavaScript pivot table component that simplifies the preparation step before visualization. It lets you group, filter, and aggregate raw data directly in the browser — then pass the result to your preferred charting library. WebDataRocks integrates out of the box with Google Charts, Highcharts, amCharts, and FusionCharts, giving you the flexibility to choose the right chart type for your audience without manual data preprocessing. Explore the available chart integrations here.

2. Ensuring Data Accuracy and Integrity

Whatever industry you are in, you must ensure your data’s accuracy and integrity. You have to make sure that all data you present visually are facts and are based on research. You must have evidence to prove and back them up when questions arise. You must double-check all the data present and be 100% sure that they are accurate. This will allow you to provide real information to your team or stakeholders and prevent you from having problems in the future.

3. Encouraging Iterative Exploration and Analysis

You improve by repeating or doing the same thing daily. The same is true for visual problem-solving. You should employ iterative exploration and analysis to ensure that you are constantly improving your decision-making. By exploring and analyzing things continually, you allow continuous adjustment to your project or business process based on the results and feedback you get.

Conclusion

All in all, data visualization truly helps in the decision-making and problem-solving process. It simplifies data and makes them easier to understand for stakeholders. It also enables more engagement from your team and allows them to gain new insights based on the data they see visually. Visual tools and platforms enhance your and your team’s decision-making abilities, providing you with more insightful solutions to solve problems and challenges.

Thank you for the article to Vy, she is a content writer for Removal.AI — an AI tool used to remove background from images for social media, eCommerce, web, app development, and marketing automation. She loves to share her insights about tech, AI, and marketing. 

What else to read?

• Data visualization with WebDataRocks & Chart.js: create a dashboard in 5 min
• Navigating the Future of Big Data and Analytics: Trends and Best Practices for 2024
• Tutorial: How to create a dashboard with WebDataRocks and AnyChart
• Top data visualization blogs to follow in 2023
• Pivot Tables: The Ultimate Guide

Recently we’ve talked about the basic principles of data visualization and charts for comparing discrete groups of data. Now it’s time to discover charts for one of the most common types of data analysis – a comparison over time.

Today we’ll dive into a process of choosing the comparison charts that display the data over a period of time and help to track trends in changes for one or multiple categories during that period. All of them prove to be extremely helpful in visualizing the performance with respect to time.

The choice of the comparison chart highly depends on how you treat time – as discrete or continuous data. It’s totally up to whether to use bars to focus on individual dates or lines to observe trends over a continuous interval.

So, let’s start!

Line chart

Line charts (also known as trend lines) are best for illustrating trends and volatility in the data. To draw a line chart, plot the data points on a Cartesian plane and connect them with a line. As a rule, time is plotted on the X-axis (as well as any other independent variables) while the values are plotted on the Y-axis.

Purpose

Use it to depict how the data changes over a continuous time period, either inside one category or among multiple ones.

Recommendations

• If the sum of values is important, consider using an area chart instead of a line chart.
• Keep it neat: don’t plot more than three-four lines per chart.

Examples and variations

• A single-line chart

Using this chart, you can compare a single metric over time.

• A multi-line chart
  

Works best for comparing values of multiple categories or groups to one another over time. To distinguish lines by categories and make the chart as readable as possible, plot lines of distinct colors and widths.

• Dual-axis line chart

It is the same as a multi-line chart but with the second Y-axis being added to it. The several usage scenarios are possible:

• For comparing two data series with the same measure which is expressed using different magnitudes (value ranges).
• For comparing two data series which values are expressed with different units of measurement (e.g., in degrees Celsius and Fahrenheit, in degrees and hours, etc).

Column chart

A column chart consists of vertical bars, the lengths of which are proportional to the values. It’s suitable not only for comparing the data between various categories but also for showing trends for the same or multiple measures over discrete time intervals.

Recommendation

If you have long data labels or/and a large number of data-series, it’s better to use a bar chart instead as it uses space more rationally.

Example

Step chart

A step chart is also known as a step graph. Its main difference from a line chart lies in a method of connecting the points. Instead of connecting the points using the shortest distance between them, it plots vertical and horizontal lines to connect the data points on a plane. Vertical lines can be interpreted as abrupt changes in time-series data.

Do you want to be the first to test the grid with your data?

Download

Purpose

Use it to track the discrete changes in the data that happen at specific moments of time.

Recommendations

• Use it with a timestamp data, or with one that has missing values for some dates.
• To communicate the magnitude of change, fill an area under the line with color.

Example

Area chart

This chart is graphed on the X- and Y-axis, with values being connected using line segments. The area between line segments and axes is filled with color.

Purpose

Use it to accentuate on how the magnitude of the cumulative value evolves over time.

Recommendations

• Assign contrast colors to different data series to help your audience compare values at glance.
• When communicating trends of multiple categories, avoid overlapping plots – this representation may be misleading for your audience. Prefer using stacked area charts instead.
• If the differences between the successive values are tiny, give preference to a line chart.

Examples and variations

• A single-series area chart

• A multi-series area chart

• A stacked area chart
  The elements for data series are stacked at each value and are rescaled to add up to 100%. This extension of the area chart is more readable, isn’t it?

Track a stock’s daily price, including gains and losses within each trading day.

Candlestick chart

A candlestick chart is a specialized way to show price movement over time, most commonly used for stocks, currencies, and other financial instruments. Each “candle” represents a fixed time interval (a day, an hour, a minute) and shows four values at once: the opening price, the closing price, the high, and the low. Candles are usually color-coded: one color when the closing price is higher than the opening price, and another when it’s lower.

Purpose

Use it to track price changes over time and spot trends in market movement. Unlike a line chart, it shows the full price range and whether prices went up or down during each period.

Recommendations

• Best suited for financial and time-series data where the open, high, low, and close all matter, not just the general trend.
• Keep the time interval consistent across all candles (all days, all hours, etc.) 
• For long time ranges with many data points, consider allowing zoom/pan so individual candles remain readable.

Example

Summary

At WebDataRocks, not only we enhance your reporting experience but share knowledge about the best modern visualization techniques.  

Hopefully, this third step of our data visualization journey helps to deepen your knowledge of the charts. We are sure that all of them deserve to take a worthy place in your presentation.

What’s next?

To keep up with all the contributions to our project, check out all the blog posts on the data visualization topic:

• Power of Data Visualization and Charts
• Best Charts to Show Discrete Data
• How to Choose Charts to Show Data Composition
• Best Charts to Show Correlation
• Best Charts to Show Data Distribution

If you’re working with data visualization, you probably have heard about PivotTable.js. It’s a lightweight, open-source JavaScript Pivot Table written by Nicola Kruchten and has been a well-known tool in data visualization for a long time.

You can explore the library yourself on its official GitHub repository, where the full source code is available, or check out a quick demo on YouTube recorded by the author to see how it works in practice.

What makes it stand out is its openness. Because it’s fully open source, you can get free access to the code and full control. It works quite well when you want to build a custom solution from the ground up.

There’s also even a React version called react-pivottable and maintained by the same author, which makes integration into modern apps a bit easier.

At the same time, the library shows its age, as it was built in 2012. It wasn’t updated at all recently, and you can feel that in the overall experience. 

This is where WebDataRocks pivot grid component takes a different direction.

It gives you a complete, polished solution out of the box. The built-in UI is already there; it looks modern, and you can integrate it into your project much faster without building everything from scratch. But even if you want to add more customization, you can also achieve it! 

As examples, we’ve got lots of showcases that demonstrate how WebDataRocks can be used in different ways: from complex analytical dashboards to daily usage habits trackers. 

• HR Analytics with WebDataRocks
• Marketing Campaigns Analysis
• WebDataRocks analytical sport Dashboard
• WebDataRocks Bento Styles Dashboard
• Santa’s Letter Management System

Even just looking at these titles, you can already see how diverse the use cases are and, for sure, it’s not even near the limit of its possibilities.

In reality, it’s a very flexible data visualization component that can be used across many areas and industries, including finance, analytics, audit, logistics, and more. Also, we’ve got some practical examples about how to use it in marketing, sales, industry, and HR analytics.  Some of the more “fun” demos are just there to show how flexible it is and how easily it can work with very different kinds of data.

In short, WebDataRocks works as a universal tool for handling data. You can structure, filter, and display it however you need. That’s why it’s useful both for serious, data-heavy projects and for more creative use cases.

What people say about PivotTable.js and WebDataRocks

PivotTable.js struggles with large datasets, which may be problematic for users. There’s no information about its limits in official docs, but some users on StackOverflow inform that while working with 2000 rows, there’s a slight delay, but with 5000 rows, it completely freezes.

On the other hand, WebDataRocks handles larger datasets (the limit is 1 MB, but it’s actually enough for most use cases) more comfortably and continues to receive updates, which makes it feel better aligned with current development needs. Also, WebDataRocks appears in numerous Reddit discussions. You may notice that people there describe it as a feature-rich component.

Flexmonster as an alternative to PivotTable.js & WebDataRocks

Even though WebDataRocks isn’t open source, it’s still completely free. Unlike many similar tools that are limited to non-profit or personal use, you’re welcome to use it in commercial projects and real business applications as well. And if you ever need something more advanced, there’s also Flexmonster – a commercial product created by the same team. It’s designed to handle even larger datasets, advanced analytics, and enterprise-level requirements.

For example, it has better performance with really large datasets, including built-in support for server-side processing. This means you can work with millions of records without loading everything into the browser, which isn’t really possible with lighter tools.

Another big advantage is the wide range of integrations. Flexmonster allows connecting to more advanced data sources, such as backend databases, and supports server-side technologies. 

Also, it supports many popular frameworks such as React, Angular, and Vue.js (which are also supported by WebDataRocks), but it doesn’t stop there. It also works with less common or newer tools like Svelte, Flutter, Blazor, Nuxt, Next.js, and much more. This makes it a very flexible choice for real-world applications.

Wrap-up

In conclusion, it really depends on what you really need. PivotTable.js is great when you value openness and want full control, especially for smaller projects. But when the goal is to move faster, work with more data, and get a clean interface without extra effort, WebDataRocks feels like a better choice.

That’s why it works so well as an alternative – not because one is completely better than another, but because they’re designed for slightly different tasks.

We are sure you’ve received many surveys on various topics and have been filling them out. But have you ever thought about how to better store this info in one place so it can be easily analyzed and navigated in the future? For sure, we’ve got a solution for you, and it’s WebDataRocks Pivot Grid!

As you know, better to learn anything by example. So, we’d love to present our new Medical Survey demo to you and walk you through the process of building it. Let’s start!

Step 1: Adding WebDataRocks Pivot Table to the Page

First step is easy as always: just add WebDataRocks to your project. It’s possible to achieve it with this code snippet:

<link href="https://cdn.webdatarocks.com/latest/theme/default/webdatarocks.min.css" rel="stylesheet" />
<script src="https://cdn.webdatarocks.com/latest/webdatarocks.toolbar.min.js"></script>
<script src="https://cdn.webdatarocks.com/latest/webdatarocks.js"></script>

Step 2: Building the Patients’ Experience Report’s Structure

Next, we create a pivot table where each row represents a patient visit, and each column reflects hospital performance metrics. In other words, just building the structure of our dashboard: include which rows, columns, and measures we’d like to display: 

const pivot = new WebDataRocks({
  container: "#pivotContainer",
  width: "100%",
  report: {
    dataSource: {
      data: getData()
    },
    slice: {
      rows: [
        { uniqueName: "Name" },
        { uniqueName: "Surname" },
        { uniqueName: "Department" }
      ],
      columns: [{ uniqueName: "Measures" }],
      measures: [
        { uniqueName: "StaffFriendly" },
        { uniqueName: "WouldRecommend" },
        { uniqueName: "VisitSuccessful" },
        { uniqueName: "FacilityClean" }
      ]
    },
    options: {
      grid: {
        type: "flat",
        showGrandTotals: false
      }
    }
  }
});

Step 3: Preparing and Preprocessing the Info for Better Data Analysis

Okay, now we need to make sure our data is ready for analysis. In our case, the survey contains many Boolean values (true/false), which are not ideal for aggregation.

To solve this, we preprocess the data by converting all Boolean values into numeric ones (1 and 0). This allows WebDataRocks to correctly calculate totals and, in the future, implement a wide variety of aggregation functions.

function preprocessData(data) {
	return data.map(function (record) {
		var result = {};
		for (var key in record) {
			result[key] =
				typeof record[key] === "boolean" ? (record[key] ? 1 : 0) : record[key];
		}
		return result;
	});
}

Now, instead of using raw data, we first transform it and then pass it to the pivot table:

dataSource: {
	data: preprocessData(getData())
}

Step 4: From Raw Data to Visual Feedback with Data Visualization Techniques

The thing is, in our survey are too many true/false answers (which are now converted into 1/0 numeric values), and they are not really readable in this format. Let’s transform them into visual signals!

And at the beginning, we just list columns that contain these boolean-type answers:

const fieldsToFormat = [
  "StaffFriendly",
  "WouldRecommend",
  "VisitSuccessful",
  "FacilityClean"
];

But the question is, in which format is better to represent them then?

Why should we even choose the one? We can apply a few of them! So, the user can pick the option that’s most convenient: pills, emojis, or colored dots.

let currentMode = "pills";

function customizeCellFunction(cell, data) {
  if (
    data &&
    data.type === "value" &&
    fieldsToFormat.includes(data.hierarchy.uniqueName)
  ) {
    if (currentMode === "pills") {
      cell.text = data.value == 1
        ? `<span class="pill yes">Yes</span>`
        : `<span class="pill no">No</span>`;
    }

    if (currentMode === "emojis") {
      cell.text = data.value == 1 ? "✅" : "❌";
    }

    if (currentMode === "dots") {
      cell.text = data.value == 1
        ? `<span class="dot yes"></span>`
        : `<span class="dot no"></span>`;
    }
  }
}

Step 5: Switching How Insights Are Displayed in an Interactive Report

Here’s the time to do switching button for it. So, just add a simple dropdown so users can change the visualization style:

<select onchange="changeDisplayMode(this.value)">
  <option value="pills">Pills</option>
  <option value="emojis">Emojis</option>
  <option value="dots">Colored Dots</option>
</select>

And, for sure, connect it:

function changeDisplayMode(mode) {
  currentMode = mode;
  pivot.customizeCell(customizeCellFunction);
}

Now it feels like completely different dashboards depending on the view:

Step 6: Styling Web Pivot Table

And now, let’s bring real medical style to our dashboard so everyone understands what the topic is at first glance!

With WebDataRocks, you don’t need to create the whole design from scratch. It has 8 already built-in themes, and look, there’s a light-blue one! I guess it’s a perfect match for our dashboard. To implement it, just add this code snippet to your project:

<link href="https://cdn.webdatarocks.com/latest/theme/lightblue/webdatarocks.min.css" rel="stylesheet" />

But don’t be scared, we don’t limit our users to just built-in themes, you can also create your own custom component theme! 

Still not enough styling? We’ve got another idea. Let’s add a header to make our dashboard even more recognizable.

<header class="page-header">
  <div class="header-inner">

    <div class="header-logo">

      <svg width="32" height="32" viewBox="0 0 32 32">
        <rect width="32" height="32" rx="8" fill="#1A6BAA" />
        <rect x="13" y="6" width="6" height="20" rx="2" fill="white" />
        <rect x="6" y="13" width="20" height="6" rx="2" fill="white" />
      </svg>
      <span class="header-brand">MediTrack</span>
    </div>

    <div class="header-title">
      <h1>Patient Visit Report</h1>
      <p>Hospital Quality &amp; Satisfaction Overview</p>
    </div>

  </div>
</header>

And for sure, don’t forget to style it properly. You can see how it all comes together in the demo.

So here it is: our dashboard is ready to help hospitals track patient experience! Hope this demo helped you learn new features of WebDataRocks and gave you ideas for implementing them in your real projects. 

By the way, #CodePenChallenge inspired the idea for this dashboard. And being honest, it’s not our first time experimenting with it. If you want to see more creative projects, we recommend reading our articles on dev.to about it: 

• #CodePenChallenge Halloween Time: Building a Pivot Table with Horror Movies using WebDataRocks
• Building a Snowy Weather Dashboard with Free Pivot Grid Library
• #CodePenChallange: Colorful Way of Data Presentation Using Data Viz Library

Recently we discussed which charts are best suited for comparison over time.

Today we’ll shed light upon the main ideas behind a data composition.

The idea of this kind of visualization lies in helping understand how individual parts comprise a whole. With data composition, it’s easy to focus attention on the importance of each part with regard to the total value.

Though most charts show the relative value, you can use some of them to compare absolute values.

Let’s move on to the charts that are best for a part-to-whole analysis.

All these charts can be divided into two categories:

• Charts that show changes in composition over time. In case you have a few periods, it’s preferred to use stacked bar or column charts. If you have a lot of time spans, give your preference to stacked area charts.
• Charts that show the static composition of the data. These include pie, waterfall charts, and treemaps.

Composition over time

Stacked area chart

A stacked area chart works the same way as an area chart, but allows comparing multiple data series by placing values of groups on top of each other. It works best if you want to show relative and absolute differences between categories.

Purpose

Use it for tracking changes of a total value across different categories over a period of time.

Recommendations

• Use the data only with positive values.
• It’s better not to use too many groups so as to avoid overlapping of plots.

Example

Measure the company’s performance over the years:

Compare with a version with overlapped plots:

The second one is neater and easier to read, isn’t it?

Stacked bar chart

You may have used a bar chart for comparing the changes in values over time or across the categories, but for displaying the size differences for parts of a whole, it’s better to take advantage of stacked bar charts. In this chart, the bars are placed on top of each other.

Purpose

Use it to show how a larger category is divided into subcategories and how each subcategory contributes to the total amount. If you need to show the percentage for each value, use a 100% stacked bar graph instead.

Recommendations

• It’s better not to use too many segments for each bar as the graph may become difficult to read and interpret.

Example

Over time, comparison of sales of books divided by genres:

Static composition

Pie chart

A pie chart is one of the most widely used types of visualization. The first mention of the pie chart is attributed to William Playfair in 1801. Though many people criticize pie charts, they play an important role when it comes to understanding the parts of the whole.

Purpose

Use it to show proportions or percentages of categories in the form of a circle divided into segments. The value of each category is proportional to the corresponding length of the arc.     

Recommendations

To make the chart comprehensible, follow these basic rules:

• It’s better not to try visualizing large amounts of data – choose up to 6 categories to make a pie chart easy to interpret.
• Make it readable – label segments outside the chart.
• Make sure your data fits this type of chart. The data should be discrete. Otherwise, incorrect use of the data may lead to misleading visualization.
• Avoid using the 3D version of the pie chart – it may distort true proportions between categories.

Example

Visualizing the categories that bring the most traffic:

Doughnut (ring) chart

A doughnut chart is a variation of the pie chart with a round hole in the center. It has the same purpose as a pie chart does, but its special feature is the capability to contain supplementary information, which can improve the readability of the chart.

Example

Shares of sales by countries:

Treemap

A treemap chart looks unpretentious, yet it’s powerful. Thanks to their compactness, treemaps help to illustrate a large amount of data in a hierarchical view.

Purpose

Use it to visualize the hierarchical data in a form of clustered rectangles which together represent the whole. Each node (or group) is represented by a rectangle, which area is proportional to the value. Colors help to add dimensions such as groups and subgroups.

Do you want to be the first to test the grid with your data?

Download

Recommendations

• Add some interactivity to the chart to achieve a detailed view of the data and be able to drill through the hierarchies (e.g., from continents to regions and countries).
• To avoid misinterpretation of your data composition analysis, it’s better not to use many parts so as not to confuse the audience.

Example

This treemap shows the number of the world population. You can drill through to know the levels of a population of each hierarchy and subhierarchy.

Funnel (pipeline) chart

A funnel chart is a type of chart that depicts the decrease at each stage in a process. In other words, it illustrates how many items are lost at each stage of a particular process. This is why it’s widely used in marketing and sales. The most popular use case is the analysis of conversions.

If there’s an increase at each stage, a funnel chart turns into a pyramid chart.

Purpose

Use it to identify bottlenecks in a workflow by ordering the entire process by stages.

Example

Conversion of website users into customers:

Waterfall chart

A waterfall chart shows how the starting value is increased or decreased by a sequence of intermediate values that lead to the total value. Such a process is called cumulative. This type of chart is commonly used in the finance domain.

Purpose

Use it to understand how the value is gradually changed over time or across categories.

Example

Final words

These visualization techniques can be effectively combined with WebDataRocks for interactive pivot table analysis and multidimensional reporting.

We do hope you enjoyed this overview of charts and recommendations. Now you are ready to pick the right type of visualization to make your data analysis be ahead of the curve.

Thank you for reading!

What’s next?

• Power of Data Visualization and Charts
• Best Charts to Show Discrete Data
• Charts for Comparison Over Time
• Best Charts to Show Correlation
• Best Charts to Show Data Distribution

In the final part of the data visualization project, we’ll discuss the charts that visualize the distribution of univariate and bivariate data.

Histogram

A histogram is the most commonly used plot type for visualizing distribution. It shows the frequency of values in data by grouping it into equal-sized intervals or classes (so-called bins). In such a way, it gives you an idea about the approximate probability distribution of your quantitative data.

Structure

The histogram is composed of vertical or horizontal bars. The height of each bar corresponds to the frequency of values that fall into this bin. By changing the bin width, you also change the number of bins – this will affect the shape of a distribution.

Purpose

To visually represent the distribution of univariate data. Additionally, with the histogram, you can figure out information about the center, spread, and skewness of data, as well as the extreme values, missing or non-typical values (outliers). In addition, you can check whether the data has multiple modes. 

One should not confuse histograms with bar or column charts – though these graphs are alike, they play totally different roles in data visualization:

• The histogram illustrates the frequency of continuous values that are grouped into ranges of a data series and represents distribution while the column chart compares values of a categorical data.
• The most noticeable visual difference is in the existence of spaces between bars: there are no spaces between bars in the histogram but they can be in the column/bar chart.
• It’s impossible to rearrange the bars in the histogram. With the column chart, it can be done without the loss of meaning.
• Columns in the column chart have equal widths but columns in the histogram – don’t. 

Example

The distribution of the country’s population:

Box and Whisker Plot

A box and whisker plot is one of the most popular charts when it comes to statistical analysis of data distribution. 

Structure

A box contains three important numbers: the first quartile, median, and third quartile. The other two numbers are the minimum and maximum – these are represented by whiskers.

These five numbers divide the dataset into sections. Each section contains around 25% of the data.

Example

Conclusion

Today you’ve learned more about charts that can be used for visualizing data distribution. We encourage you to learn by doing and try creating such charts in your data analysis project.

Analyzing data distribution with pivot tables

A useful starting point for distribution analysis is summarizing your raw data into frequency groups. With WebDataRocks, you can quickly aggregate datasets and export the results to build histograms or box plots in your preferred charting library. Check out the WebDataRocks + Google Charts integration for a practical example.

What’s next?

Eager to learn about other chart types? You are welcome to read the previous blog posts of the data visualization project:

• Power of Data Visualization and Charts
• Best Charts to Show Discrete Data
• Charts for Comparison Over Time
• How to Choose Charts to Show Data Composition
• Best Charts to Show Correlation

References

• Histogram in Google Charts
• Box Plots in plotly.js

The right choice of web reporting tool can be a crucial factor for any data-driven business. Software development is a truly complex process that requires managing and coordinating many different modules and components for a reporting solution to work flawlessly.  So in this article, we decided to dive into the exciting world of JavaScript libraries and components for report applications, web reporting tools, or software with reporting elements and created a collection of the best JavaScript libraries for report applications.